Introduction to Cognitive Economics

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Holt, Charles A., Experimental Economics , Princeton University Press, Princeton, 1993. Friedman, Daniel - Cassar, Alessandra. Economics Lab. An intensive course in experimental economics , Routledge, London and New York, 2004. Guala, Francesco - Motterlini, Matteo, cL 9economia cognitiva e sperimentale d, Università di Trento . Jones, Martin - Sudgen, Robert, cPositive confirmation bias in the acquisition of information d, Theory and Decision , 50, 2001, 59-99. Kagel, John H. - Roth, Alvin E., The Handbook of Experimental Economics , Princeton University Press, Princeton, 1997. Kahneman, Daniel - Tversky, Amos, cProspect Theory: An Analysis of Decision under Risk d, Econometrica , 47, 1979, 263- 91. Kenning, Peter 3 Plassman, Hilke, cNeuroeconomics c, University of Munster, 2005 Kosfeld, Michael - Heinrichs, Markus - Zak, Paul J. 3 Fischbacher, Urs 3 Fehr, Ernst, cOxytocin increases trust in humans c Nature , 2005. McCabe, Kevin - Houser, Daniel - Ryan, Lee - Smith, Vernon 3 Trouard, Theodore, dA functional imaging study of cooperation in two-person reciprocal exchange d Proc. Nat. Ac. Sci. USA 2001. Rustichini, Aldo, cNeuroeconomics: Present and Future d, Games and Economic Behavior , 52, 2005, 201-212. Smith, Vernon, cEconomics in the Laboratory d, Journal of Economic Perspectives , 8, 1994, 113-131 . van Dijk, Eric - van Knippenberg, Daan, cBuying and selling exchange goods: Loss aversion and the endowment effect d, Journal of Economic Psychology , 17 (1996) 517-524. 2 1. COGNITIVE AND BEHAVIOURAL ECONOMICS " BEHAVIOURAL OR COGNITIVE ECONOMICS DEFINITIONS Cognitive economics is not a distinct subfield of economics but a school of thought based on the idea that the study of economic behaviour has to be founded on the interdisciplinary approach characterizing cognitive sciences According to a well known definition, the field of Cognitive Science is formed by the intersection of a variety of different disciplines including cognitive psychology , philosophy of mind , linguistics , artificial intelligence and neuroscience Behavioural Economics is a discipline adds to the other ones because it takes into cognitive science a legacy of specific tools and theories cBecause economics is the science of how resources are allocated by individuals and by collective institutions like firms and markets, the psychology of individual behavior should underlie and inform economics, much as physics informs chemistry; archaeology informs anthropology; or neuroscience informs cognitive psychology. However, economists routinely 4and proudly 4use models that are grossly inconsistent with findings from psychology. A recent approach, 8 8behavioral economics, 9 9 seeks to use psychology to inform economics, while maintaining the emphases on mathematical structure and explanation of field data that distinguish economics from other social sciences d (Camerer 1999) Behavioural economics would be a reunification of psychology and economics and it would preserve the distinctive emphasis on formal models and descriptive statistics that characterizes mainstream economics In fact, there are two key issues that contemporary economics has to deal with: o first, the inconsistency of the predictions of most economic models with the experimental results of psychology; o secondly, the rigidity of mathematical structure of that same models joined with the indefiniteness of the theoretical implications of the statistical data collected in the field 3 METHODOLOGY A prevalent view among behavioural economists - but not endorsed here - is that behavioural economics emerges as the study of deviations from the paradigm of rational choice. cBehavioral economics applies models of systematic imperfections in human rationality, to the study and engineering of organizations, markets and policy. These imperfections include limits on rationality, willpower and self-interest and any other behavior resulting from an evolved brain with limited attention. d (Camerer 2006) Behavioural economics would be the result of relaxing the assumption of perfect rationality that pervades mainstream economics. A different view is that cognitive economics is characterized by a specific methodological approach to the study of economic behaviour. If the field of cognitive economics is, almost by definition, the analysis of the mental and cognitive processes through which the economic agent collects, processes, interprets and uses information and knowledge to make economic choices, it assumes the role of trait d 9union between economics and psychology. Its main object is to open the black-box containing all the processes through which preferences are formed and are translated into choices. In this light cognitive economics is different from behavioural economics, whose methodology is based on the analysis of the effectively exhibited behaviours. The behaviourism is consonant with the axiom of revealed preferences which allows ignoring any psychological determinant of behaviour in economics Behavioural economics approach is a clear departure from the cas if d approach endorsed by Milton Friedman. 4 cF-twist d argument combines two criteria: 1. Theories should be judged by the accuracy of their predictions; 2. Theories should not be judged by the accuracy of their assumptions. Because theories with patently false assumptions can make surprisingly accurate predictions, economic theories that assume that individual agents are highly rational and wilful, judge probabilities accurately, and maximize their own wealth might prove useful, even though psychology shows that those assumptions are systematically false. The F twist allowed economists to ignore psychology. The empirically-driven approach to behavioural economics agrees with criterion (1) and rejects criterion (2). In fact, criterion 2 is rejected because of the primacy of criterion 1, based on the belief that replacing unrealistic assumptions with more psychologically realistic ones should lead to better predictions. NORMATIVE PURPOSES Policy consequences: Because rational people make few mistakes, policies aren 9t necessary to help them. Relaxing rationality assumptions therefore permits reasoned argument about how people can be helped. For example, if people weight the future hyperbolically rather than exponentially, they will impulsively buy goods they will soon regret having bought. A good policy to help those who weight the future hyperbolically is a mandatory 8 8cooling off 9 9 period that permits 8 8hot 9 9 consumers to renege on purchase decisions for a short period of time, such as 3 days. Cooling-off policies exemplify 8 8conservative paternalism 9 9 4 they will do much good for people who act impulsively and cause very little harm (an unnecessary 3-day wait) for those who do not act impulsively; thus, even conservatives who resist state intervention should find them appealing. 5 2. EXPERIMENTAL ECONOMICS POINTS OF VIEW PROS cWould it not be better to leave laboratory experiments to psychologists who are trained to run them properly? Nobody doubts that we have a great deal to learn from psychologists about laboratory technique and learning theory and learning theory, but recent history would nevertheless suggest that the answer is a resounding no . Our comparative advantage as economists is that we not only understand the formal statements of economic theory, but we are also sensitive to the economic environments and institutions within which the assumptions from which such statements are deduced are likely to be valid. Just as chemists know not to mix reagents in dirty test tubes, so we know that there is no point in testing economic propositions in circumstances to which they should not reasonably be expected to apply. d (Binmore 1999) cOnce models, as opposed to economies, became the focus of research the simplicity of an experiment and perhaps even the absence of features of more complicated economies became an asset. The experiment should be judged by the lessons it teaches about theory and not by its similarity with what nature might happen to have created. d (Plott 1991) CONS 1- experimental situations often project a gamelike atmosphere in which a 8subject 9 may see himself as 8matching wits 9 against the experimenter 2- experimental subjects are cast in roles and they can act in accordance with his (mis)perceptions of these roles 3- experiments have too short horizons (real world lasts many years and many trials) 4- human beings are capable to control their behavior through the implementation of abstract rules (Cross 1994) " The laboratory is not a socially neutral context, but is itself an institution with its own formal or informal, explicit or tacit, rules " Human agency takes place within a socio-economic world that is structured in the sense that it consists of internally-related positions and systems " Experimentation in economics is likely to be of limited value, save for situations 3 such as auctions 3 that exist in conditions of relative isolation and are characterised by low internal complexity (Siakantaris 2000) 6 2.1 LABORATORY METHODS DATA SOURCES HOW? WHERE? Happenstance (uncontrolled conditions - ongoing processes) Experimental (controlled conditions - deliberately created) Field (naturally occurring environment) National Accounts Commodity Prices Income Maintenance Experiments Field Experiments Laboratory (artificial environment) Casual Processes in the Lab Discovery of Penicillin Choice Experiments Auctions Simulation Laboratory Asset Markets EXPERIMENTAL ECONOMICS LABORATORY EXPERIMENTS (artificial environment) (controlled ad hoc conditions) PURPOSES OF THE EXPERIMENT (WHY?) (Davis-Holt, Experimental Economics 1994) 1) Test of Behavioral Hypotheses. by constructing a laboratory environment that satisfies as many of the structural assumptions of a particular theory, it is possible to verify its behavioral implications 2) Theory Stress Tests to examine the sensitivity of a theory to violations of obviously unrealistic assumptions 3) Searching for Empirical Regularities heuristic experiments to discover and document stylized facts (Roth 1986) a) Speaking to Theorists b) Searching for Facts c) Whispering in the Ears of Princes 7 EXPERIMENTAL METHODOLOGY (HOW?) A) PROCEDURAL REGULARITY to permit replications that the researcher and observers would accept as being valid - instructions - subject pool and methods of recruiting subjects - experimental physical environment - computerized or manual B) MOTIVATION Induced-value theory: use of a reward medium allows to induce prespecified characteristics in experimental subjects and to make subjects 9 innate characteristics largely irrelevant - monotonicity: subjects prefer more reward medium to less and not become satiated - salience: rewards are explicitly and unambiguously connected to the decisions made - dominance: changes in subjects 9 utility from the experiment come mainly from the reward medium and other subjective costs or benefits are rendered negligible by comparison, i.e. others 9 reward C) UNBIASEDNESS Experiments should be conducted in a manner that does not lead participants to perceive any particular behavioural pattern as being correct or expected, unless explicit suggestion is a treatment variable - double blind setting D) CALIBRATION The design has to pre-specify and to cleanly separate the experimental predictions of alternative theories. E) DESIGN PARALLELISM Results established in the lab hold in other, especially non-lab, real-world situations where similar ceteris paribus conditions hold Do not replicate in the lab the complexities of a field environment (which has infinite details) or the precise assumptions of a formal model (which usually leave out details) Vernon Smith 9s parallelism precept (1982) cPropositions about the behavior of individuals and the performance of institutions that have been tested in laboratory microeconomics apply also to nonlaboratory micro economies where similar ceteris paribus conditions hold d - presumption of external validity Charles Plott (1982): cWhile laboratory processes are simple in comparison to naturally occurring processes, they are real processes in the sense that real people participate for real and substantial profits and follow real rules in doing so. It is precisely because they are real they are interesting d 8 PROFESSIONAL SUBJECTS OR STUDENTS? Main Subjects pool: undergraduate or MBA students Advantages 1. readily accessible 2. low opportunity costs 3. steep learning curve 4. they do not know much about experimenter 9s hypothesis PhD students: unreliable subjects because they get interested in what are you doing and respond to their understanding of your topic rather than to incentives you have constructed Class students or friends: dominance or salience at risk, conflicts between personal, teaching and scientific aims Professional subjects: comparisons show that students are more adept at maximizing their profits and learning in the lab 3 high opportunity costs 3 prespecified and innate characteristics are too strong: when involved in laboratory markets they attempt to apply rules of thumb, which, valuable for dealing with uncertainty in the parallel natural market, are meaningless guides in the lab. Burns (1985): professional wool buyers and students in a progressive auction (professionals apply familiar rules and not adjust to design requirements) Dyer, Kagel, and Levin (1985): bidding behavior of students and construction workers (no difference) Dejong et al (1988): Businessmen and students in sealed-offer markets (some profits, but higher variance for businessmen) What about gender, age, risk attitude, experience? A bit of history 9 Kagel, John H. - Roth, Alvin E. The Handbook of Experimental Economics Princeton University Press, Princeton, 1997 INDEX a) public goods (Ledyard) cooperation vs. selfishness (social dilemmas, free-riding, institutions) what improves cooperation (thresholds, learning) b) coordination problems (Ochs) experiments with overlapping generations coordination games with Pareto ranked equilibria decentralized matching environments c) bargaining experiments (Roth) agreements causes of disagreements and costly delays bargaining protocol and preplay communications d) industrial organization (Holt) trading institutions centralized and decentralized monopoly regulation and potential entry market structure and market power collusion factors product differentiation and multiple markets e) experimental asset markets (Sunder) informational efficiency of markets state-contingent claims and bubbles learning and dynamics of adjustment paths investment and public policy f) auctions (Kagel) symmetric independent private-values models common value auctions collusion g) individual choice behavior 10 I NDIVIDUAL C HOICE B EHAVIOR I. Judgment " A. Calibration o 1. Scoring Rules o 2. Confidence Intervals " B. Perception and Memory Biases " C. Bayesian Updating and Representativeness o 1. Underweighting of Base Rates o 2. Underweighting on Likelihood Information (Conservatism) o 3. The Law of Small Numbers and Misperceptions of Randomness " D. Confirmation Bias and Obstacles to Learning " E. Expectations Formation " F. Iterated Expectations and the Curse of Knowledge o 1. False Consensus and Hindsight Bias o 2. Curse of Knowledge " G. The Illusion of Control II. Choice under Risk and Uncertainty " A. Mounting Evidence of Expected Utility Violation (1965-1986) o 1. The Allais Paradoxes o 2. Process Violations o 3. Prospect Theory o 4. Elicitation Biases " B. Generalizations of Expected Utility and Recent Tests o 1. Predictions of Generalized EU Theories o 2. Empirical Studies Using Pairwise Choices o 3. Empirical Studies Measuring Indifference Curves o 4. Empirical Studies Fitting Functions to Individuals o 5. Cross-Species Robustness: Experiments with Animals " C. Subjective Expected Utility o 1. The Ellsberg Paradox o 2. Conceptions of Ambiguity " D. Choice over Time " E. Description Invariance o 1. Framing Effects o 2. Lottery Correlation, Regret, and Display Effects o 3. Compound Lottery Reduction " F. Procedure Invariance o 1. New Evidence of Preference Reversal o 2. Arbitrage and Incentives o 3. Reversals and Markets o 4. Social Comparisons and Reversals " G. Endowment Effects and Buying-Selling Price Gaps o 1. Market Experiments o 2. Endowment Effects: Some Psychology and Implications " K. Search o 1. Search for Wages and Prices o 2. Search for Information 11 2.2 BIASES IN JUDGMENT cPeople rely on heuristic principles which reduce the complex tasks of assessing probabilities and predicting values to simpler judgmental operations. In general, these heuristics are quite useful, but sometimes they lead to severe and systematic errors d (Tversky and Kahneman 1974) " CONFIRMATION BIAS Once individuals devise a strong hypothesis they will tend to misinterpret or even misread new information unfavourable to this hypothesis Also production of treatment effects: when a researcher believes a hypothesis is true, he often produces a biased sample of evidence that reinforces his or her belief (unconsciously?) Consequence is obvious: confirmation bias inhibit learning whether one 9s underlying belief is false But also Fresh thinkers may be better at seeing solutions to problems than people who have meditated at length on the problems, because the fresh thinkers are not overwhelmed by the cinterference d of old hypotheses. Correlated phenomena o F ALSE CONSENSUS : People use their own tastes and beliefs as information in guessing what others like and believe - Application: to put in other people 9s shoes is not useful to find focal points o H INDSIGHT BIAS : current recollections of past judgments tend to be biased by what actually happened since then 3 Application: adaptive expectations vs. rational expectations 12 AN ILLUSTRATIVE EXPERIMENT Martin Jones and Robert Sugden cPositive confirmation bias in the acquisition of information d, Theory and Decision , 50, 2001, 59-99 o Positive confirmation bias : tendency, when testing an existing belief, to search for evidence which could confirm that belief, rather than for evidence which could disconfirm it o Application to economic learning: an agent who repeatedly faces the same set of options might retain the false belief that a particular option was optimal, even after long exposure to evidence which, rationally interpreted, would indicate the contrary Wason 9s (1968) selection task Four double-sided cards Subjects are told that each card has a letter on one side and a number on the other, but they can see only the upper faces of the four cards Four cards show 8A 9, 8D 9, 84 9 and 87 9 Each subject is asked to consider the following rule , as applied to the four cards: 8If a card has a vowel on one side, then it has an even number on the other side 9 Instruction: 8Your task is to say which of the cards you need to turn over to find out whether the rule is true or false 9 Two most common responses are the 8A 9 card alone, and the 8A 9 and 84 9 cards in combination The correct answer to the question posed is, of course, the combination of 8A 9 and 87 9 The frequently-chosen 84 9 card can provide no information which is relevant to the issue of whether the rule is true or false The 8A 9 and 84 9 cards are the ones that are capable of providing evidence which confirms the rule: by turning over either of these cards, the subject may find a card with a vowel on one side and an even number on the other In contrast, the 87 9 card can only disconfirm the rule (i.e. by revealing a card which has a vowel on one side but not an even number on the other) In this sense, the evidence from the selection task can be interpreted as consistent with positive confirmation bias 13 Criticism: The original selection task was formulated in highly abstract terms Counterargument: Correct response might be facilitated by adding thematic content to the task, i.e. by providing a cover story which accounts for the statement and gives some point to the selection task Jones and Sudgen 9s design o Subjects have to pay a fixed cost per card turned over o After she has made this choice, the cards she has chosen are turned over o She then makes the judgment that the statement is 8true 9 or 8false 9 o Finally the remaining cards are turned over and she receives a fixed reward if and only if her judgment was in fact correct Experiment carried out at the University of East Anglia in Norwich 120 students recruited on the campus (wide range of courses) Computerized experiment No communication between subjects Each task is presented by means of a sequence of six screens The screen presents first the cover story, then the statement and finally four cards to choose Each object has two characteristics, each of which can take one of two values that correspond with p, ¬ p, q, and ¬ q (as before vowel and consonant, even and odd) Each subject perform seven different tasks < p, ¬ q > or < ¬ q, p > , if turned over, is a disconfirmation of the experimental HP < p, q > and < q, p > are confirmations Exemplificative Tasks 1. Relatives A survey is taken of 100 people in Los Angeles, Seattle, London and Norwich who have relatives living in other cities. Each person in the survey living in Britain has relatives in Los Angeles or Seattle and each person living in America has relatives in Norwich or London. No one has relatives in more than one city. The details of the survey are written down on report cards by putting the city each person lives in on one side of the card and the city their relatives live in on the other side. A sample of four report cards is selected. Look at whichever cards you wish to test the statement: [Standard statement] Every person in the sample who lives in London also has a relative who lives in Los Angeles. [Contraposed statement] Every person in the sample who lives in Seattle also has a relative who lives in Norwich. 14 2. Drinkers Only people over the age of eighteen are allowed to drink alcohol in a pub in Britain. A survey is carried out of 100 people in a large public house which identifies their age and whether they are drinking alcohol or a soft drink. Each person 9s details are put down on a report card with the person 9s age on one side and their drinking behaviour on the other. A sample of four report cards is selected. To find out if the four people in the sample are obeying the law, look at whichever cards you wish to test the statement: [Standard statement] Every person in the sample who is drinking alcohol is also over eighteen. [Contraposed statement] Every person in the sample who is under eighteen is also drinking a soft drink. Results In favour of the confirming bias hypothesis: 62% of the choices (445/720) <No cards> 18% 14% <p, q> 18% Conclusions Overwhelming evidence that subjects 9 information-gathering decisions are systematically biased in favour of information which is potentially confirming But behaviour seems to have been closer to Bayesian rationality than in many other selection task experiments Especially the drinkers story facilitates Bayesian rationality (why?) What is the effect of financial incentives ? 15 2.3 CHOICE UNDER RISK AND UNCERTAINTY What do we mean by rational choice in economics? Lots of formulations, involving assumptions of different strength Different forms of rationality imply different experiments to test them " Goal oriented " Satisficing behaviour " Maximizing behaviour " Ordinal utility maximization " Expected utility maximization " Subjective expected utility maximization Experimental economics reveals the hidden or implicit assumption by showing anomalies in the formulation of rationality 16 Razionalità in un contesto economico: gli agenti economici utilizzano l 9informazione disponibile in modo da operare la scelta ottimale date le alternative disponibili e gli obiettivi prefissati. Approccio più usato: massimizzazione dell 9utilità (soggettiva) attesa in condizioni di rischio (von Neumann 3 Morgenstern, 1947) Quale azione è razionale in condizioni di rischio? Il profilo di un agente razionale è definito per mezzo degli assiomi dell 9utilità attesa . Tre assiomi o principi fondamentali " assioma di ordinamento (A1), " assioma di continuità (A2), " assioma di indipendenza (A3). X, y, z, w,& i risultati (detti anche cconseguenze d o cstati del mondo d) di una lotteria o prospetto probabilistico; f la relazione di preferenza p valore di probabilità. Ò cse& allora d (2) cse e soltanto se d ¬ cnon d (A1) f è una relazione d 9ordine: (x f y) Ò ¬(y f x) [asimmetria] (x f y & y f z) Ò (x f z) [transitività] (A2) (x f y f z) Ô [px + (1 3 p)z f y f qy + (1 3 q)z ] per p e q strettamente fra 0 e 1 [continuità] 17 (A3) Per qualsiasi p tale che 0 + assiomi classici del calcolo della probabilità Teorema di rappresentazione dell 9utilità attesa se una relazione di ordinamento (f) soddisfa (A1), (A2), (A3), allora esiste una funzione reale di utilità U(.) (definita sui risultati delle lotterie) tale che per tutte le lotterie X e Y: (1) X f Y Ô EU(X) f EU(Y), dove l 9utilità attesa EU ( Expected Utility ) è data dalla somma delle utilità moltiplicate per le probabilità dei risultati di una lotteria: (2) EU = £ i p i U(x i ) quindi in economia un agente è 8razionale 9 se massimizza EU . Il modello di von Neumann e Morgenstern è applicabile a situazioni di rischio e può essere esteso (Savage 1954) alle situazioni di incertezza , in cui le probabilità rappresentano gradi di credenza ( beliefs ) individuali, ( cteoria dell 9utilità attesa soggettiva d) Economia neoclassica come scienza del comportamento razionale. Egoismo : gli agenti economici massimizzano la propria utilità e sono indifferenti riguardo a quella altrui; materialismo : l 9utilità degli agenti economici dipende soltanto dalla quantità di beni consumati; utilità decrescente al margine : l 9utilità cresce col numero di beni (a più beni corrisponde più utilità che a meno beni) ma diminuisce al margine (al consumo del bene n +1 corrisponde meno utilità che al bene n ) 18 2.4 CHOICE ANOMALIES EXPERIMENTS WITHIN SUBJECTS [ Source: Tversky and Kahneman 1981, Thaler 1980] Experiment 1 (certainty effect) Which of the following options do you prefer? A. A sure win of $30 [78%] EV 30 B. An 80% chance to win $45 [22%] 36 Which of the following options do you prefer? C. A 25% chance to win $30 [42%] EV 7.5 D. A 20% chance to win $45 [58%] 9 0,20×U(45) > 0,25×U(30) Ò U(45 )/U(30) > 0,25/0,20 0,80×U(45) < 1×U(30) Ò U(45 )/U(30) < 1/0,80 but 0,25/0,20 = 1/0,80 Experiment 2 (loss aversion) Imagine that you face the following pair of concurrent decisions. First examine both decisions; then indicate the options you prefer: Decision (i). Choose between A. A sure gain of $240 [84%] EV +240 B. 25% chance to gain $1,000 and 75% chance to lose nothing [16%] +250 Decision (ii). Choose between C. A sure loss of $750 [13%] -750 D. 75% chance to lose $1,000 and 25% chance to lose nothing [87%] -750 Experiment 3 (mental accounting) Choose between E. 25% chance to win $240 and 75% chance to lose $760 [0%] -510 F. 25% chance to win $250 and 75% chance to lose $750 [100%] -500 But E = A&D and F = B&C Experiment 4 (shoes costs) Imagine that you are about to purchase a jacket for ($125)[$15] and a calculator for ($15)[$125]. The calculator salesman informs you that the calculator you wish to buy is on sale for ($10)[$120] at the other branch of the store, a 20-minute drive away. Would you make the trip to the other store? Yes: 16% No: 84% Experiment 5 (sunk costs) Imagine that you have decided to see a play, admission to which is $10 per ticket. As you enter the theater you discover that you have lost a $10 bill. Would you still pay $10 for the ticket to the play? [Yes: 88% No: 12%] Now imagine that you have decided to see a play and paid the admission price of $10 per ticket. As you enter the theatre you discover that you have lost your ticket. The seat was not marked and the ticket cannot be recovered. Would you pay $10 for another ticket? [Yes: 46% No: 54%] 19 BUYING-SELLING PRICE GAP A simple class experiment " Half of you - randomly chosen - is named as cowners d and receive a windfall gift of a classy, stylish, desirable HBS pencil. You are asked to examine it closely. It is yours to keep, or to sell " The remaining half do not receive a pencil and is refereed to as cnon-owners d " Then each owner is asked to pass his/her pencil to a neighbouring non-owner, so that the non-owners can also fully examine the pencil. " It may exist some possible gains from trade. In order to assess this, the experimenter wants to elicit from each owner the minimum price at which he/she would be willing to sell the pencil and from each non-owner, the maximum price she/he would be willing to pay to buy the pencil. " Experimental result: Owner prices (WTA) > Non-Owner prices (WTP) Economic theory predicts that the prices a person will pay to buy and sell an object should be the same. Environmental economists in the 1970s first discovered that this is not true: duck hunters would pay $ 247 to maintain a wetland suitable for ducks but asked $ 1,044 to give up the wetland (Hammack J. and Brown G. M. Water fowl and wet lands: Toward bio economic analysis , John Hopkins University Press, 1974) Students were willing to pay 2.75 on average for college mugs but they asked for 5.25 to sell their mugs (Kahneman, Daniel, Jack L. Knetsch, and Richard H. Thaler, cExperimental Tests of the Endowment Effect and the Coase Theorem, d JPE 1990) EXPLANATIONS Plott 9s (1996) discovered preferences hp.: individuals may discover their valuations for unfamiliar items during the elicitation process Economic factors: income effects and substitution, transaction costs, implied value of the good, profit motivation Psychological factors: endowment effect , legitimacy, ambiguity and moral responsibility 20 " ENDOWMENT EFFECT People prefer the things they own, ceteris paribus (but what about the neighbour 9s grass is always greener than yours?) Explanations - action error (Ritov-Baron 1991): fear of action errors is a bias in favour of inaction - higher sensitivity to overpaying (out-of-pocket costs) than to selling too cheaply (opportunity costs) (Thaler 1980) - disposition effect (Weber-Camerer 1992): reluctance to take actions leading to irreversible loss and eagerness to take actions creating gains Ex: the volume of houses sold falls when housing prices fall - status quo bias (Samuelson-Zeckhauser 1988): if you have a current choice you enhance preferences for it - prospect theory 9s loss aversion (Tversky-Kahneman 1988): losses are more painful than equally sized gains are pleasurable - action is different from giving advices: no endowment effect when people advise others (Marshall-Knetsch-Sinden 1988) Consequences Invalidates the Coase theorem: the valuation of a property right is not independent of who owns the right 3 contracting parties allocate efficiently rights and duties if there is no transaction cost 21 AN ILLUSTRATIVE EXPERIMENT Eric van Dijk - Daan van Knippenberg, cBuying and selling exchange goods: Loss aversion and the endowment effect d, Journal of Economic Psychology , 17 (1996) 517-524 The object of the experiment: to refute Kahneman and Tversky 9s theory that the endowment effect is not particularly common in markets where goods are specifically bought for exchange Experimental design Sixty-six undergraduate students (35 females; 31 males) randomly assigned to the different experimental conditions and paid for their participation. Half of the participants received a bargaining chip representing money to be exchanged for money at the end of the experiment. Two treatments: 1) Fixed Exchange Value conditions: participants learned they could exchange the chip with the experimenter for Dfl. 3.50 (1 Dutch Guilder = $.55 US) 2) Uncertain Exchange Value conditions, participants learned they could exchange the chip for an amount of money between Dfl. 1.75 and Dfl. 5.25, depending on a chance procedure. Participants could trade the bargaining chips among themselves: participants owning a chip (the Sellers) could sell this chip to participants not owning a chip (the Buyers). On a separate form, prices were listed from Dfl. 0.25 to Dfl. 6.75 (with Dfl. 0.25 intervals). Sellers were requested to indicate for each price whether or not they would sell at that price. Buyers indicated for each price whether or not they would buy at that price. The experimenter would randomly select a price on this form, thus establishing the 'market price' for the chip (procedure to prevent participants from misstating their true values 3 subjects had to stick to their stated intention) After the experimenter collected the forms, participants estimated the value of the bargaining chip. At this point the experiment was ended. Participants were debriefed and all received Dfl. 5.00 22 RESULTS The selling price of the sellers (mean = Dfl. 3.76) exceeded the buying price of the buyers (mean = Dfl. 3.05). As predicted, this main effect was qualified by a significant Position X Uncertainty interaction (F(1,62) = 4.1, p < 0.05). Corroborating the findings of Kahneman et al. (1990), no significant endowment effect was observed when the value of the exchange good was fixed (F(1,62)= 1.2, p < 0.3; overall mean = Dfl. 3.40; mean for sellers = Dfl. 3.56; mean for buyers = Dfl. 3.25). In agreement with our hypothesis, in the case of an uncertain exchange rate, however, the selling price (mean = Dfl. 3.97) significantly exceeded the buying price (mean = Dfl. 2.87; F(1,62) = 15.7, p < 0.0001). These results indicate that exchange goods may, like consumption goods, be susceptible to the endowment effect, provided that exchange rates are uncertain. IMPLICATIONS endowment effect on consumption goods (e.g., mugs, chocolate bars) in situations where it may be difficult to compute the net gains and losses of trade: if someone wants to buy your chocolate bar you may perceive giving up the chocolate bar as a loss. But if someone offered you one and a half chocolate bars for your chocolate bar it is easy to compute the net gains of trade and you would probably not be susceptible to the endowment effect. Research should not only focus on what is being traded (e.g., exchange goods or consumption goods) but on what is being traded for what. Insights related to the characteristics approach of Lancaster (1971), i.e. goods as bundles of characteristics Lancaster 9s theory: goods are more substitutable the more characteristics they have in common In a similar vein, people would be less subject to the endowment effect the more characteristics the objects traded have in common because it is easier to compute the net gains and losses of trade. Future research : to investigate the relation between substitutability and the endowment effect by comparing the willingness to trade for goods varying in the number of common characteristics 23 " PREFERENCE REVERSAL Prices subjects gave for bets are highly correlated with bet payoffs but choices are more highly correlated with probabilities Slovic-Lichtenstein 1968 Subjects were offered two bets with the same expected values: p-bet with high probability and low payoff $-bet with low probability and high payoff Subjects choose the p-bet , but when asked to state the lowest price at which they would be willing to sell each gamble if they owned it, they put a higher price on the $-bet p-bet 8/9% to win $4 $-bet 1/9% to win $40 Choice p-bet [71%] Price $-bet > Price p-bet [67%] Standard analysis of choice assumes procedure invariance : A is preferred to B if A is selected when B is available or if A has a higher reservation price than B. These two procedures have to give rise to the same ordering In the laboratory, different methods of eliciting preference often give rise to systematically different orderings Explanations - violations of transitivity - violations of procedure invariance (pricing is different from choosing) - violations of the independence axiom BDM procedure (Becker, De Groot, Marschak 1964) After the subject states a selling price for a gamble, an offer is generated by some random process. The subject receives the offer if it exceeds the stated selling price, and plays the gamble if the stated price exceeds the offer. The price stated by the subject, therefore, serves only to determine whether the subject will play the bet or receive the cash, but it does not determine the actual amount. If the subject is an expected utility maximizer, this procedure is incentive compatible: the decision maker has no incentive to state a selling price that departs from his or her actual cash equivalent 24 AN ILLUSTRATIVE EXPERIMENT The compatibility hypotheses (Slovic, Griffin and Tversky 1990) stimulus-response compatibility: the weight of a stimulus attribute in judgment or in choice is enhanced by its compatibility with the response scale " If there is no compatibility effort and error may reduce the impact of the stimulus " A response mode tends to focus attention on the compatible features of the stimulus Experimental design " Subjects were given two pieces of information about each of 12 large companies, i.e., the company 9s 1986 market value and the company 9s rank among the Top 100 with respect to 1987 profits " Half of the subjects asked to predict the 1987 market value in billions of dollars whereas the other half were asked to predict the company 9s rank with respect to its 1987 market value " Each subject had one predictor measured on the same scale (that is, money or rank) as the dependent variable and one predictor measured on a different scale Results " As implied by compatibility each predictor was given more weight when the predicted variable was expressed on the same scale " As a consequence, the relative weight of the 1986 market value was twice as high for those who predicted in dollars than for those who predicted the corresponding rank " This effect produced many reversal in which one company was ranked above another but the order of their predicted value was reversed Other experimental design " If preference reversal are due primarily to the compatibility of prices and payoffs both expressed in dollars, their incidence should be substantially reduced by the use of nonmonetary outcomes, i.e. a one-week pass for all movie-theatres in town or a dinner for two at a good restaurants " Results: the prevalence of preference reversal was reduced by nearly 50 percent Conclusions The compatibility hypothesis implies that the payoffs, which are expressed in the same units, will be weighted more heavily in pricing bets than in choosing between bets. Furthermore, since the payoffs of L bets are much larger than the payoffs of H bets, the major consequence of a compatibility bias is the overpricing of the low-probability high- payoff bets 25 3. CONSTRUCTIVE REACTIONS Main finding of experimental economics: there is a variety of definitions of rational individual (what about decision makers 9 heterogeneity?) Risk neutral economic man: never buys insurance, but would be willing to pay any finite amount to participate in Petersburg paradox. Expected utility maximizing man: buys insurance, but ignores sunk costs, and is immune to framing effects. Almost rational economic man (e.g. prospect theory man) has malleable reference points and probability perceptions, but still has preferences - comfortable with non-utility Allais choices, but doesn 9t do preference reversals. Psychological man doesn 9t have preferences, has mental processes. Different frames and contexts, and different choice procedures elicit different processes - So he may sometimes exhibit preference reversals because choosing and pricing elicit different mental procedures. Neurobiological man: doesn't (even) have a fixed collection of mental processes, in the sense of psychological man. He has biological and chemical processes which influence his behaviour. Different blood chemistry leads to different mental processes; e.g. depending on the level of lithium (or Valium or Prozac) in his blood, he makes different decisions (on both routine matters and matters of great consequence - even life and death). An understanding of how chemistry interacts with mental processes has proved to be very useful, for instance in treating depression. Expected utility theory ? prospect theory ? asymmetric response to price increases, downward-sloping labour supply among cab drivers Exponential discounting ? hyperbolic discounting ? addition and procrastination Self-seeking behaviour ? social utilities ? trust and reciprocity in financial relationship Equilibrium ? processes of equilibration ? drift effect, automata ranked preferences ? constructed preferences ? information manipulation in horse race betting, void informational cascades in the artistic markets Bayesian probability judgments ? confirmation bias ? Self-fulfilling expectations in financial markets, focal points 26 " Alternative theories to explain anomalies Prospect theory (Tversky and Kahneman 1991) Conception of rationality alternative to expected utility maximization - Machina 9s (1989) non expected utility, Gilboa and Schmeidler (2006) case based decision theory " Attempts to reconcile rational theory and irrationality in experiments it does not take much rationality to behave nearly optimally in an experimental market - Gode and Sunder 9s (1993) zero intelligence agents in simulated experimental markets lead to nearly efficient outcomes artificial intelligence and connessionism to study learning processes within the experiments 3 Cox and Grether (2005) endogenous closs aversion d discovery of preference by watching others to construct efficient experimental markets in which individual irrationality may persist but is reduced by market forces 3 Camerer and Weber (1990) " Heuristic experiments : searching for new facts heterogeneous agents models: the abandonment of the fictitious construct of representative agent local network analysis 3 complex dynamic systems characterized by dispersed interaction among agents acting locally on each other in some space empirically-driven analysis à la Schelling neuroeconomics 27 3.1 PROSPECT THEORY Kahneman and Tversky Econometrica 1979 Tversky and Kahneman Journal of Risk and Uncertainty 1992 Experimental evidence a) people perceive the outcome of a monetary prospect in terms of the variations (positive or negative) related to a non-constant reference level (usually the status quo ) rather than in terms of absolute levels of wealth b) people appear to be more adverse to losses, relatively to their reference level, than how they are attracted by the winning of the same value. The disutility of the monetary loss x is lower than the utility of winning the same amount x . Consequently, reaction to losses is stronger than the reaction to winnings. Prospect Theory postulates the existence of two functions - the value function v and the weight function (or decisions weights) p - such as the decision maker strictly prefers X a Y iff ( ) ( ) ( ) ( ) i i i i y v q x v p > 1 1 À À where x i = x i 3 x 0 is the variation associated to a prospect x i with respect to a reference point x 0 . 28 Differences between prospect theory (PT) and subjective expected utility theory (SUET) 1) the decision maker is not interested in the final status per sé (SUET) but at the change of status ( x i ) with regard to the reference point ( x 0 ) (PT) 2) the value function v is concave ( crisk averse d) for gains and convex ( crisk seeking d) for losses (PT). 3) the value function v is steeper around the reference point for losses than for gains ( closs aversion d). 4) the psychological sensitivity to losses and gains diminishes marginally: incremental winnings/losses give decreasing marginal utility/disutility 5) while in SUET the utility of any possible event is weighted with his probability, in PT the value of any welfare change is multiplied by a cdecision weight d, that is not a probability but a probability transformation. Probability transformations do not follow probability rules and cannot be interpreted as degree of beliefs. They are obtained by choices and measure the impact of events on prospects 9 desirability and not the perceived probability of events. 6) the weight function p is monotone, increasing, and discontinuous between 0 and 1, because it systematically overweighs very low probabilities and under weights medium and high probabilities ( ccertainty effect d) 29 3.2 CONNESSIONISM AND NEURAL NETWORKS Approccio computazionale allo studio della mente e del cervello Costruzione di un modello del cervello in base all 9ipotesi che esiste una relazione (isomorfismo) tra la struttura del cervello e la struttura dei processi cognitivi Connessionismo come simulazione di sistemi complessi formati da insiemi interconnessi di semplici unità di elaborazione Rete neurale come modello esplicativo approssimativo dei processi di rappresentazione e di apprendimento che avvengono nel cervello e quindi come formalizzazione delle concezioni connessioniste in materia di processi cognitivi Obiettivo: formazione di rappresentazioni mentali delle azioni economicamente rilevanti e descrizione dei processi di apprendimento Hebb (1949), The Organization of Behavior , New York, Wiley - spiega il comportamento psicologico in termini di funzionamento del cervello superando il dualismo tra mente e cervello - processi cognitivi sono il frutto di particolari modalità di connessione tra neuroni formanti catene lungo le quali viaggia un segnale elettrofisiologico - assemblee cellulari come gruppo di neuroni tra loro interconnessi: l 9eccitazione di un solo neurone si comunica a tutti gli altri del gruppo - fenomeni come percezione, memoria, apprendimento spiegati come processi combinatori di unità elementari e non come rapporto stimolo/risposta (approccio comportamentista) Cenni all 9organizzazione sistema nervoso Sistema nervoso come rete specifica (composta di parte aventi tra di loro legami funzionali) che funziona attraverso impulsi elettrici trasmessi attraverso cellule chiamate neuroni La rete riceve gli input dai recettori (sensi) che convertono gli stimoli provenienti dal corpo e dal mondo esterno in pattern , e cioè configurazioni, di impulsi elettrici che trasportano le informazioni nella rete Pattern interagiscono con l 9insieme di impulsi che già viaggiono nella rete neurale e provocano l 9emisione di impulsi che controllano gli effettori , quali i muscoli, le ghiandole, nel determinare le risposte. 30 Neurone come cellula che contiene il nucleo che è portatore cromosomico del patrimonio ereditario. Dal nucleo partono i dendriti (che veicolano i segnali di input) e gli assoni che veicolano i segnali di output Le sinapsi sono i punti di contatto tra due neuroni I nervi conducono gli impulsi dagli organi recettori agli effettori, l 9impulso viaggia in un primo neurone lungo un assone e genera un impulso nel secondo neurone attraverso i dendriti. Le comunicazioni tra neuroni avvengono mediante rilascio di sostanze chimiche dette neurotrasmettitori Un neurotrasmettitore è una sostanza che nel sistema nervoso trasporta segnali tra neuroni attraverso le sinapsi chimiche. I neurotrasmettitori possono essere eccitatori o inibitori , cioè possono rispettivamente promuovere la creazione di un impulso nervoso nel neurone ricevente o inibire l'impulso. 31 HP di partenza: le scelte economiche sono in gran parte basate su comparazioni tra beni o tipologie (classi di beni) o patterns Processo di apprendimento descritto come modificazione dell 9intensità (o dei pesi) delle connessioni sinaptiche, modificazioni in parte genetiche e in parte acquisite. Problema di pattern recognition , categorizzazione come attività primaria attraverso il quale i soggetti danno un significato ai nuovi stimoli e identificano il significato di simboli conosciuti Funzionamento e organizzazione delle reti neurali Neuronodo £ come unità elementare costitutiva della rete neurale Ogni input X i ha un certo impatto su un neuronodo £ misurato da un peso W i - che nei neuroni è la connessione sinaptica L 9output è pari alla somma dei valori (pesi) degli input che sono espressi vettorialmente. Gruppi di neuronodi costituiscono una rete neurale artificiale AMBIENTE ESTERNO SCHEMA DI UNA RETE NEURALE ARTIFICIALE UNITA 9 DI OUTPUT OUTPUT AMBIENTE ESTERNO OUTPUT £ X 1 W 1 X W 2 X n W n 32 Nella fase di apprendimento i pesi possono essere modificati in risposta ai vari input Se la somma dei pesi degli input supera un valore predefinito di soglia il neuronodo si attiva L 9apprendimento secondo lo schema neurale consiste nel rafforzare alcune connessioni ed estinguerne altre La rete elabora le informazioni accettando patterns e cioè configurazioni e forme in input ai nodi di input e produce una forma in output Esempio illustrativo : Teoria del consumatore Assunzione standard: il consumatore ha un ben definito sistema di preferenze ma può essere incerto sull 9utilità da attribuire al consumo di uno o più beni Assunzione alternativa: il consumatore apprende consumandoi in sequenza un insieme di beni in modo da testare sperimentalmente l 9utilità ricavabile da essi Hp modalità apprendimento: il consumatore costruisce un insieme di classi-tipo di beni e assegna ogni bene specifico ad una determinata classe Ogni bene specifico è una combinazione di caratteristiche Processo di memorizzazione per caratteristiche dei beni Bene rappresentabile come un vettore: [ ] 3 3 3 3 4 2 \xd \xd \xd \xd \xe \xc » ² ± » ² ± p p p ,..., , dove p p p » ² ± ,..., , sono i pesi delle caratteristiche ± , ² , » Il peso di ogni caratteristica varia in funzione delle esperienze di consumo passate che hanno effetto sul valore che il consumatore attribuisce ai vari beni valutati come combinazioni di caratteristiche Hp: consumatore opera come un perceptron , che è la forma più semplice di rete neurale, che impara a classificare le forme di input in categorie di appartenenza e impiega una funzione di attivazione che ha la forma seguente 1 0 \xf = 0 1 ) ( x f L 9attivazione (1) del neuronodo si verifica se i valori del vettore di input superano il livello di soglia v e il consumatore giudica che il bene appartiene ad una certa classe, altrimenti il neuronodo rimane inattivo (0) 33 3.3 ARTIFICIAL INTELLIGENCE W. Brian Arthur cDesigning Economic Agents that Act like Human Agents: A Behavioral Approach to Bounded Rationality d, The American Economic Review , 81, 1991 pp. 353-391 Standard approach in economics to modelling limited rationality: to lay down axioms and assumptions that suppose limits to economic agents 9 computational ability or memory, and investigate their consequences Alternative approach: to develop theoretical (virtual) economic agents that act and choose in the way actual humans do Agents are represented as using parametrized decision algorithms so that the agents 9 behaviour matches real human observed in the same decision context Calibrated agents furnish predictions based on actual agents rather than idealized behaviour Modelling gives a repertoire of calibrated algorithms to cover the various contexts that might arise and to reproduce statistically the characteristics of human choice, including the distinctive errors or departures from rationality that humans make Application: Iterated choice under uncertainty A decision maker chooses one of n possible actions at each time t Actions give a random payoffs or profits drawn from a stationary distribution that is unknown in advance Agent chooses one alternative at each time, observes its consequence or payoff and over time updates his choice as a result In this setting there is a trade-off between exploitation of high-payoffs actions and exploration of seldom-tried actions that potentially may have higher payoffs 34 Automata 9s process of learning: learning as the updating of the probabilities of taking each action on the basis of the payoffs or outcomes experienced Action i brings reward ¦ ( i ), that is unknown to the agent in advance, positive and distributed randomly with a stationary distribution The artificial agent learns by means of the following simple algorithm: it associates a vector or strengths S I with the actions I=1,&.,N at each time t, where the current sum of these strengths is C t and p t is the agent 9s probabilities of taking actions 1 through N at time t Four-step decision process (at time t) 1- the agent calculates the probability vector as the relative strengths associated with each action, it sets p t = S t /C t 2- chooses one action j from the set according to the probabilities p t and triggers that action 3- observes the payoff received and updates strength by adding the chosen action j 9s payoff to action j 9s strength, i.e. it sets the strengths to S t + ² t where ² t = ¦ ( j ) e j (e j is the jth unit vector) 4- renormalizes the strengths to sum to a value from a prechosen time sequence. In this case it renormalizes strengths to sum to C t =Ct v In this way the rate of learning is proportional to 1/( Ct v ) and parameters C and v define a two-parameter family of algorithms that can be used to calibrate the automaton Behavioural interpretation of the algorithm The strength vector summarizes the current confidence the agent or automaton has learned to associate with actions 1 through N. Confidence associated with an action increases according to the random payoff it brings in when taken. S 0 is the initial confidence in the action which represents prior beliefs carried over from past experiences 35 Connectionistic interpretation of the algorithm Algorithm as a set of N classifiers each competing to be activated where the classifier J is the simple couple cIf is time to act/choose alternative j d . One classifier is triggered on the basis of current strengths and the chosen classifier 9s strength is updated by the associated reward. Algorithm nonlinearity Actions that are frequently taken are further reinforced (to permit exploitation of useful actions) Algorithm stocasticity Actions triggered randomly and rewards are drawn randomly from a distribution (to allow for exploration) What about the long-run properties of the system? There is a tradeoff between two events: 1) If an inferior highpayoff action is triggered early, it gains immediately in strength and may dominate other superior actions 2) If exploration does not disappear too fast, it uncover that there are better actions than the inferior ones Arthur 9s (1991) main result: the probability of choosing action i grows at a rate proportional to the difference between the expected payoff and the average payoff at current probabilities plus an unbiased perturbation term Calibration of C and v parameters for three purposes a- representation of actual human behaviour b- if the measured value of v lies within the range that guarantees asymptotically optimal choices c- characteristics of learning (speed and ability to discriminate) Arthur (1991) uses Robillard experiment to calculate v=0.00 C=31.1 and obtains a good fit between automata and human beings (see Figures 1-2 p. 356) 36 In Herrnstein et al. (1990) the distribution of payoffs is no longer fixed but depends instead on the frequency of actions taken In this case human behaviour 3 that is replicated by artificial agents - shows the characteristic of melioration : their choices do not converge to optimal frequencies that maximize expected payoff, but to quite different frequencies that equalize expected payoffs of each action What about optimal convergence? The experimental and artificial result is that the likelihood of convergence to Nash depends on the difficulty of discrimination among the action payoffs. Human choice, if captured by the calibration, appears to cdiscover d and exploit the optimal action with high probability, as long as it is not difficult to discriminate. Beyond a certain perceptual threshold, where differences in alternatives become less pronounced, nonoptimal outcomes become more likely Economic applications In simulated financial markets the calibrated agents learn to buy and sell stock appropriately and that stock price indeed converges to small fluctuations around the rational expectations value But also speculative bubbles and crashes may occur Conclusions It is possible to design artificial learning agents and calibrate their crationality d to replicate human behaviour This simulation can also reproduce two stylized facts that are well-known to psychologists: a) with frequency-dependent payoffs humans cmeliorate d rather than optimizing b) there is a threshold in discrimination among payoffs below which humans may lock in to suboptimal choices The simulation shows that humans may converge to Nash equilibrium but may also select inferior choices and explore less known alternatives. There is a characteristic learning time for human decisions because behaviour does not settle before 40 to 100 more trials. 37 4. NEUROECONOMICS 4.1 Definitions and tools Neuroeconomics is the grounding of microeconomics in details of neural functioning. While the revealed preferences approach has deliberately avoided trying to discover the neural determinant of choices, neuroscience is beginning to allow direct measurement of thoughts and feelings Methodologically, neuroeconomics is not intended to test economic theory in a traditional way - particularly under the view that utilities and beliefs are only revealed by choices - but to establish the neural circuitry underlying economic decisions, for the eventual purpose of making better predictions. Starting points " Much of the brain is constructed to support automatic processes which are faster than conscious deliberation and which occur with little or no awareness or feeling of effort " Economic behaviour is under the pervasive and often unrecognized influence of finely tuned affective (emotion) systems that are localized in particular brain region " If affective systems are damaged or perturbed by brain damage, stress, imbalances in neurotransmitters, alcohol or cthe heat of the moment d the deliberative system generally is not capable of getting the job done " Many behaviours that are clearly established to be caused by automatic or affective systems are interpreted by human subjects, spuriously, as the product of cognitive deliberation " The deliberative system, which is the system that is responsible for making sense of behaviour, does not have perfect access to the output of the other systems, and exaggerates the importance of processes it understands when it attempts to make sense of the body 9s behaviour. Consequence: behaviour emerges from the interplay between controlled and automatic systems on the one hand and between cognitive and affective systems on the other 38 Main tools of analysis Brain imaging Comparison of people performing different tasks (an "experimental" task and a "control" task) by observing the images of the regions of the brain that are differentially activated by the experimental task. Changes in electric currents (to measure electrical activity of the brain) Changes in metabolism (to measure neural metabolism processes) Electro-encephalogram (EEG) Magnetencephalogaphy (MEG) Positron emission topography (PET) Functional transcranial Doppler-Sonography (FTCD) Functional magnetic resonance imaging (fMRI) " Electro-encephalogram (or EEG) uses electrodes attached to the scalp to measure electrical activity synchronized to stimulus events or behavioural responses known as Event Related Potentials, or ERPs (unobtrusiveness and precise temporal

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